Skip to main content
SpringerLink
Log in

EEG Signal Processing: Theory and Applications

  • Chapter
  • First Online:
Neural Engineering

Abstract

The electroencephalogram (EEG) is a dynamic noninvasive and relatively inexpensive technique used to monitor the state of the brain. The International Federation of Clinical Neurophysiology defines the EEG as “(1) the science relating to the electrical activity of the brain, and (2) the technique of recording electroencephalograms” [1]. EEG has a number of clinical uses that range from monitoring normal wakefulness or arousal states to complex clinical situations involving seizure or coma. The brain contains unique information in many regions at any given time. An EEG signal recorded with electrodes placed on the scalp consists of many waves with different characteristics. Arrays of electrodes are distributed over the entire scalp. The large amount of data recorded from even a single EEG electrode pair presents a difficult interpretation challenge. Signal processing methods are needed to automate signal analysis and interpret the signal phenomena.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Steriade M, Gloor P, Llinas RR et al (1990) Report of IFCN Committee on basic mechanisms. Basic mechanisms of cerebral rhythmic activities. Electroencephalogr Clin Neurophysiol 76(6):481–508

    Article  Google Scholar 

  2. Clark J (1993) The origin of biopotentials. In: Webster J (ed) Medical instrumentation: application and design. Wiley, New York

    Google Scholar 

  3. Niedermeyer E (1999) The normal EEG of the waking adult. In: Niedermeyer E, da Silva FL (eds) Electroencephalography: basic principles, clinical applications, and related fields. Williams & Wilkins, Baltimore

    Google Scholar 

  4. Fisch BJ (1999) Fisch & Spehlmann’s EEG primer: basic principles of digital and analog EEG. Elsevier, Amsterdam

    Google Scholar 

  5. Rowan AJ, Tolunsky E (2002) Primer of EEG. Butterworth-Heinemann, Philadelphia

    Google Scholar 

  6. Adeli H, Ghosh-Dastidar S, Dadmehr N (2007) A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy. IEEE Trans Biomed Eng 54(2):205–11

    Article  Google Scholar 

  7. Ghosh-Dastidar S, Adeli H (2009) A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection. Neural Netw 22(10):1419–31

    Article  Google Scholar 

  8. Ghosh-Dastidar S, Adeli H, Dadmehr N (2007) Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection. IEEE Trans Biomed Eng 54(9):1545–51

    Article  Google Scholar 

  9. Ghosh-Dastidar S, Adeli H, Dadmehr N (2008) Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection. IEEE Trans Biomed Eng 55(2 Pt 1):512–8

    Article  Google Scholar 

  10. Csaba J (2003) Positron emission tomography in presurgical localization of epileptic foci. Ideggyogy Sz 56(7–8):249–54

    Google Scholar 

  11. Millan E, Abou-Khalil B, Delbeke D et al (2001) Frontal localization of absence seizures demonstrated by ictal positron emission tomography. Epilepsy Behav 2(1):54–60

    Article  Google Scholar 

  12. Ohtsuka Y, Yoshinaga H, Kobayashi K et al (2001) Predictors and underlying causes of medically intractable localization-related epilepsy in childhood. Pediatr Neurol 24(3):209–13

    Article  Google Scholar 

  13. San Pedro EC, Mountz JM, Ojha B et al (2000) Anterior cingulate gyrus epilepsy: the role of ictal rCBF SPECT in seizure localization. Epilepsia 41(5):594–600

    Article  Google Scholar 

  14. Meltzer CC, Adelson PD, Brenner RP et al (2000) Planned ictal FDG PET imaging for localization of extratemporal epileptic foci. Epilepsia 41(2):193–200

    Article  Google Scholar 

  15. Lantz G, Michel CM, Seeck M et al (1999) Frequency domain EEG source localization of ictal epileptiform activity in patients with partial complex epilepsy of temporal lobe origin. Clin Neurophysiol 110(1):176–84

    Article  Google Scholar 

  16. Rektor I, Svejdova M, Kanovsky P et al (1997) Can epileptologists without access to intracranial EEG use reliably the International League Against Epilepsy classification of the localization-related epileptic syndromes? A stereo-electroencephalographic study. J Clin Neurophysiol 14(3):250–4

    Article  Google Scholar 

  17. Ebersole JS, Pacia SV (1996) Localization of temporal lobe foci by ictal EEG patterns. Epilepsia 37(4):386–99

    Article  Google Scholar 

  18. Sutherling WW, Levesque MF, Crandall PH et al (1991) Localization of partial epilepsy using magnetic and electric measurements. Epilepsia 32(Suppl 5):S29–40

    Google Scholar 

  19. Sherman D, Zhang N, Garg S et al (2011) Detection of nonlinear interactions of EEG alpha waves in the brain by a new coherence measure and its application to epilepsy and anti-epileptic drug therapy. Int J Neural Syst 21(2):115–26

    Article  Google Scholar 

  20. Sinai A, Bowers CW, Crainiceanu CM et al (2005) Electrocorticographic high gamma activity versus electrical cortical stimulation mapping of naming. Brain 128(Pt 7):1556–70

    Article  Google Scholar 

  21. Lesser RP, Kaplan PW (1994) Long-term monitoring with digital technology for epilepsy. J Child Neurol 9(Suppl 1):S64–70

    Article  Google Scholar 

  22. Arroyo S, Lesser RP, Gordon B et al (1993) Functional significance of the mu rhythm of human cortex: an electrophysiologic study with subdural electrodes. Electroencephalogr Clin Neurophysiol 87(3):76–87

    Article  Google Scholar 

  23. Iasemidis LD (2011) Seizure prediction and its applications. Neurosurg Clin N Am 22(4):489–506, vi

    Article  Google Scholar 

  24. Iasemidis LD, Schachter S, Worrell GA et al (2011) Dynamics and neuromodulation of the epileptic brain. Introduction. Int J Neural Syst 21(2):v–vi

    Article  Google Scholar 

  25. Lotte F, Congedo M, Lecuyer A et al (2007) A review of classification algorithms for EEG-based brain–computer interfaces. J Neural Eng 4(2):R1–R13

    Article  Google Scholar 

  26. Curran EA, Stokes MJ (2003) Learning to control brain activity: a review of the production and control of EEG components for driving brain–computer interface (BCI) systems. Brain Cogn 51(3):326–36

    Article  Google Scholar 

  27. Wolpaw JR, Birbaumer N, Heetderks WJ et al (2000) Brain–computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng 8(2):164–73

    Article  Google Scholar 

  28. Kubanek J, Miller KJ, Ojemann JG et al (2009) Decoding flexion of individual fingers using electrocorticographic signals in humans. J Neural Eng 6(6):066001

    Article  Google Scholar 

  29. Schalk G, Leuthardt EC, Brunner P et al (2008) Real-time detection of event-related brain activity. Neuroimage 43(2):245–9

    Article  Google Scholar 

  30. Schalk G, Miller KJ, Anderson NR et al (2008) Two-dimensional movement control using electrocorticographic signals in humans. J Neural Eng 5(1):75–84

    Article  Google Scholar 

  31. Allison BZ, McFarland DJ, Schalk G et al (2008) Towards an independent brain–computer interface using steady state visual evoked potentials. Clin Neurophysiol 119(2):399–408

    Article  Google Scholar 

  32. Schalk G, Brunner P, Gerhardt LA et al (2008) Brain–computer interfaces (BCIs): detection instead of classification. J Neurosci Methods 167(1):51–62

    Article  Google Scholar 

  33. Caro XJ, Winter EF (2011) EEG biofeedback treatment improves certain attention and somatic symptoms in fibromyalgia: a pilot study. Appl Psychophysiol Biofeedback 36(3):193–200

    Article  Google Scholar 

  34. Nagai Y (2011) Biofeedback and epilepsy. Curr Neurol Neurosci Rep 11(4):443–50

    Article  MathSciNet  Google Scholar 

  35. Arjunan SP, Kumar DK, Jung TP (2009) Changes in decibel scale wavelength properties of EEG with alertness levels while performing sustained attention tasks. Conf Proc IEEE Eng Med Biol Soc 2009:6288–91

    Google Scholar 

  36. Kaplan PW (2004) Stupor and coma: metabolic encephalopathies. Suppl Clin Neurophysiol 57:667–80

    Article  Google Scholar 

  37. Kaplan PW (2004) The EEG in metabolic encephalopathy and coma. J Clin Neurophysiol 21(5):307–18

    Google Scholar 

  38. Kaplan PW, Genoud D, Ho TW et al (1999) Etiology, neurologic correlations, and prognosis in alpha coma. Clin Neurophysiol 110(2):205–13

    Article  Google Scholar 

  39. Kaplan PW, Genoud D, Ho TW et al (2000) Clinical correlates and prognosis in early spindle coma. Clin Neurophysiol 111(4):584–90

    Article  Google Scholar 

  40. Luu P, Tucker DM, Englander R et al (2001) Localizing acute stroke-related EEG changes: assessing the effects of spatial undersampling. J Clin Neurophysiol 18(4):302–17

    Article  Google Scholar 

  41. Murri L, Gori S, Massetani R et al (1998) Evaluation of acute ischemic stroke using quantitative EEG: a comparison with conventional EEG and CT scan. Neurophysiol Clin 28(3):249–57

    Article  Google Scholar 

  42. Braun JC, Hanley DF, Thakor NV (1996) Detection of neurological injury using time-frequency analysis of the somatosensory evoked potential. Electroencephalogr Clin Neurophysiol 100(4):310–8

    Article  Google Scholar 

  43. Crone NE, Hao L (2002) Functional dynamics of spoken and signed word production: a case study using electrocorticographic spectral analysis. Aphasiology 16:903–26

    Article  Google Scholar 

  44. Crone NE, Miglioretti DL, Gordon B et al (1998) Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. II. Event-related synchronization in the gamma band. Brain 121(Pt 12):2301–15

    Article  Google Scholar 

  45. Kearse LA Jr, Manberg P, Chamoun N et al (1994) Bispectral analysis of the electroencephalogram correlates with patient movement to skin incision during propofol/nitrous oxide anesthesia. Anesthesiology 81(6):1365–70

    Article  Google Scholar 

  46. Kearse LA Jr, Manberg P, DeBros F et al (1994) Bispectral analysis of the electroencephalogram during induction of anesthesia may predict hemodynamic responses to laryngoscopy and intubation. Electroencephalogr Clin Neurophysiol 90(3):194–200

    Article  Google Scholar 

  47. Nunez P (2006) Electric fields of the brain. Oxford, New York

    Book  Google Scholar 

  48. Dubisar BM, Stoner SC, Khan R et al (2002) Seizures and extrapyramidal symptoms in a patient with Tourette’s syndrome, Asperger’s syndrome, and multiple sclerosis treated with interferon beta-1a and clomipramine. Pharmacotherapy 22(11):1504–7

    Article  Google Scholar 

  49. Munschauer FE 3rd, Kinkel RP (1997) Managing side effects of interferon-beta in patients with relapsing-remitting multiple sclerosis. Clin Ther 19(5):883–93

    Article  Google Scholar 

  50. Lombroso CT (2007) Neonatal seizures: gaps between the laboratory and the clinic. Epilepsia 48(Suppl 2):83–106

    Article  Google Scholar 

  51. Clancy RR (1993) Differential diagnosis and contribution of EEG. In: Wright LL, Merenstein GB, Hirtz D (eds) Reports of the workshop on acute perinatal asphyxia in term infants. NIH, US Dept of HHS, Rockville, MD, pp 115–121

    Google Scholar 

  52. Klekowicz H, Malinowska U, Piotrowska AJ et al (2009) On the robust parametric detection of EEG artifacts in polysomnographic recordings. Neuroinformatics 7(2):147–60

    Article  Google Scholar 

  53. Malinowska U, Durka PJ, Zygierewicz J et al (2007) Explicit parameterization of sleep EEG transients. Comput Biol Med 37(4):534–41

    Article  Google Scholar 

  54. Malinowska U, Durka PJ, Blinowska KJ et al (2006) Micro- and macrostructure of sleep EEG. IEEE Eng Med Biol Mag 25(4):26–31

    Article  Google Scholar 

  55. Durka PJ, Matysiak A, Montes EM et al (2005) Multichannel matching pursuit and EEG inverse solutions. J Neurosci Methods 148(1):49–59

    Article  Google Scholar 

  56. Durka PJ (2003) From wavelets to adaptive approximations: time–frequency parametrization of EEG. Biomed Eng Online 2:1

    Article  Google Scholar 

  57. Durka PJ, Szelenberger W, Blinowska KJ et al (2002) Adaptive time–frequency parametrization in pharmaco EEG. J Neurosci Methods 117(1):65–71

    Article  Google Scholar 

  58. Blinowska KJ, Durka PJ (2001) Unbiased high resolution method of EEG analysis in time–frequency space. Acta Neurobiol Exp (Wars) 61(3):157–74

    Google Scholar 

  59. Limpiti T, Van Veen BD, Attias HT et al (2009) A spatiotemporal framework for estimating trial-to-trial amplitude variation in event-related MEG/EEG. IEEE Trans Biomed Eng 56(3):633–45

    Article  Google Scholar 

  60. Shahidi Zandi A, Tafreshi R, Javidan M et al (2010) Predicting temporal lobe epileptic seizures based on zero-crossing interval analysis in scalp EEG. Conf Proc IEEE Eng Med Biol 5537–5540

    Google Scholar 

  61. Derya Ubeyli E (2009) Statistics over features: EEG signals analysis. Comput Biol Med 39(8):733–41

    Article  Google Scholar 

  62. Gustafsson LL, Ebling WF, Osaki E et al (1996) Quantitation of depth of thiopental anesthesia in the rat. Anesthesiology 84(2):415–27

    Article  Google Scholar 

  63. Jospin M, Caminal P, Jensen EW et al (2007) Detrended fluctuation analysis of EEG as a measure of depth of anesthesia. IEEE Trans Biomed Eng 54(5):840–6

    Article  Google Scholar 

  64. Paul JS, Patel CB, Al-Nashash H et al (2003) Prediction of PTZ-induced seizures using wavelet-based residual entropy of cortical and subcortical field potentials. IEEE Trans Biomed Eng 50(5):640–8

    Article  Google Scholar 

  65. Kardel T, Stigsby B (1975) Period-amplitude analysis of the electrodencephalogram correlated with liver function in patients with cirrhosis of the liver. Electroencephalogr Clin Neurophysiol 38(6):605–9

    Article  Google Scholar 

  66. Uchida S, Feinberg I, March JD et al (1999) A comparison of period amplitude analysis and FFT power spectral analysis of all-night human sleep EEG. Physiol Behav 67(1):121–31

    Article  Google Scholar 

  67. Armitage R, Hoffmann R, Fitch T et al (1995) A comparison of period amplitude and power spectral analysis of sleep EEG in normal adults and depressed outpatients. Psychiatry Res 56(3):245–56

    Article  Google Scholar 

  68. Ktonas PY, Gosalia AP (1981) Spectral analysis vs. period–amplitude analysis of narrowband EEG activity: a comparison based on the sleep delta-frequency band. Sleep 4(2):193–206

    Google Scholar 

  69. Feinberg I, Fein G, Floyd TC (1980) Period and amplitude analysis of NREM EEG in sleep: repeatability of results in young adults. Electroencephalogr Clin Neurophysiol 48(2):212–21

    Article  Google Scholar 

  70. Hjorth B (1970) EEG analysis based on time domain properties. Electroencephalogr Clin Neurophysiol 29:306–310

    Article  Google Scholar 

  71. Hofmann WG, Spreng MP (1997) Unsupervised classification of EEG from subdural seizure recordings. Brain Topogr 10(2):121–32

    Article  Google Scholar 

  72. Mouze-Amady M, Horwat F (1996) Evaluation of Hjorth parameters in forearm surface EMG analysis during an occupational repetitive task. Electroencephalogr Clin Neurophysiol 101(2):181–3

    Article  Google Scholar 

  73. Maragos P, Kaiser JF, Quatieri TF (1993) Energy separation in signal modulations with application to speech analysis. IEEE Trans Signal Process 41:3024–3051

    Article  MATH  Google Scholar 

  74. Kaiser JF (1990) On a simple algorithm to calculate the “energy” of a signal. Proc. IEEE, Albuquerque, New Mexico, pp 381–384

    Google Scholar 

  75. Maragos P, Potamianos A (1995) Higher order differential energy operators. IEEE Sig Process Lett 2:152–154

    Article  Google Scholar 

  76. Fang J, Atlas L (1995) Quadratic detectors for energy estimation. IEEE Trans Signal Process 43(11):2582–2594

    Article  Google Scholar 

  77. Nikias CL, Petropulu AP (1993) Higher order spectral analysis: a nonlinear signal processing framework. Prentice Hall, Englewood Cliffs, NJ

    Google Scholar 

  78. Niedermeyer E, Sherman D, Geocadin R (1999) The burst suppression electroencephalogram. Clin Electroencephalogr 30(3):99–105

    Google Scholar 

  79. Chatrian G, Bergamini L, Dondey M et al (1974) A glossary of terms most commonly used by clinical electroencephalographers. Electroencephalogr Clin Neurophysiol 37:538–548

    Article  Google Scholar 

  80. Cottenceau V, Petit L, Masson F et al (Nov, 2008) The use of bispectral index to monitor barbiturate coma in severely brain-injured patients with refractory intracranial hypertension. Anesth Analg 107(5):1676–82

    Article  Google Scholar 

  81. Prins SA, de Hoog M, Blok JH et al (2007) Continuous noninvasive monitoring of barbiturate coma in critically ill children using the bispectral index monitor. Crit Care 11(5):R108

    Article  Google Scholar 

  82. Krishnamurthy KB, Drislane FW (1999) Depth of EEG suppression and outcome in barbiturate anesthetic treatment for refractory status epilepticus. Epilepsia 40(6):759–62

    Article  Google Scholar 

  83. Sherman DL, Brambrink AM, Ichord RN et al (1999) Quantitative EEG during early recovery from hypoxic-ischemic injury in immature piglets: burst occurrence and duration. Clin Electroencephalogr 30(4):175–83

    Google Scholar 

  84. da Silva FL (1999) EEG analysis: theory and practice. In: Niedermeyer E, da Silva FL (eds) Electroencephalography: basic principles, clinical applications and related fields. Williams & Wilkins, Baltimore, MD

    Google Scholar 

  85. Kay SM (1988) Modern spectral estimation: theory and application. Prentice Hall, Englewood Cliffs, NJ

    MATH  Google Scholar 

  86. Goel V, Brambrink AM, Baykal A et al (1996) Dominant frequency analysis reveals brain’s response to injury and recovery. IEEE Trans BME 43:1083–1092

    Google Scholar 

  87. Burg JP (1975) Maximum entropy spectral analysis. Unpublished dissertation, Department of Statistics, Stanford University, Stanford, CA

    Google Scholar 

  88. Marple SL (1987) Digital spectral analysis with applications. Prentice Hall, Englewood Cliffs, NJ

    Google Scholar 

  89. Goel V (1995) A novel technique for EEG analysis: application to neonatal hypoxia-asphyxia. MS, BME, Johns Hopkins University, Baltimore, MD

    Google Scholar 

  90. Schmidt RO (1986) Multiple emitter location and signal parameter estimation. IEEE Trans Antennas Propagat AP-34:276–280

    Article  Google Scholar 

  91. Kronland-Martinet R, Morlet J, Grossmann A (1987) Analysis of sound patterns through wavelet transforms. Int J Pattern Recog Artificial Intell 1:273–302

    Article  Google Scholar 

  92. Hlawatsch F, Boudreaux-Bartels GF (1992) Linear and quadratic time–frequency signal representations. IEEE Sig Proc Mag 9:21–67

    Article  Google Scholar 

  93. Rioul O, Vetterli M (1991) Wavelet theory: mapping signal to a time-scale plane. IEEE Sig Process Mag 8(4):14–39

    Article  Google Scholar 

  94. Hlawatsch F, Boudreaux-Bartels G (1992) Linear and quadratic time–frequency signal representations. IEEE Sig Process Mag 9:21–62

    Article  Google Scholar 

  95. Meste O, Rix H, Jane P et al (1994) Detection of late potentials by means of wavelet transform. IEEE Trans Biomed Eng 41:625–634

    Article  Google Scholar 

  96. Grossmann A, Morlet J (1984) Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM J Math Anal 15:723–736

    Article  MathSciNet  MATH  Google Scholar 

  97. Unser M, Aldroubi A (1996) Wavelets in medicine and biology. CRC, Boca Raton, FL

    MATH  Google Scholar 

  98. Chan YT (1995) Wavelet basics. Kluwer Academic, Boston, MA

    Book  Google Scholar 

  99. Holschneider M (1995) Wavelets: an analysis tool. Clarendon Press, Oxford

    MATH  Google Scholar 

  100. Holschneider M, Kronland-Martinet R, Tchamitchian P (1989) A real-time algorithm for signal analysis with the help of the wavelet transform. In: Combes J, Grossmann A, Tchamitchian P (eds) Wavelets: time-frequency methods and phase space. Springer, New York

    Google Scholar 

  101. Najmi AH, Sadowsky J (1994) The continuous wavelet transform and variable resolution time–frequency analysis. J Hopkins APL Tech D 18(1):134–140

    Google Scholar 

  102. Sadowsky J (1994) The continuous wavelet transform: a tool for signal investigation and understanding. J Hopkins APL Tech D 15(4):306–318

    Google Scholar 

  103. Jones DL, Baraniuk RG (1991) Efficient approximation of continuous wavelet transforms. Electron Lett 27(9):748–750

    Article  Google Scholar 

  104. Vetterli M, Kovacevic J (1995) Wavelets and subband coding. Prentice Hall, Englewood Cliffs, NJ

    MATH  Google Scholar 

  105. Goupillaud P, Grossmann A, Morlet J (1984) Cycle-octave and related transforms in seismic signal analysis. Geoexploration 23:85–102

    Article  Google Scholar 

  106. Oppenheim AV, Schaffer RW (1989) Discrete time signal processing. Prentice Hall, Englewood Cliffs, NJ

    MATH  Google Scholar 

  107. Thakor NV, Sherman D (1995) Wavelet (time-scale) analysis in biomedical signal processing. In: Bronzino JD (ed) Biomedical engineering handbook. CRC, Boca Raton, FL

    Google Scholar 

  108. Shannon CE (1948) A mathematical theory of communication. Bell System Tech J 27:623–656

    MathSciNet  Google Scholar 

  109. Bezerianos A, Tong S, Thakor NV (2003) Time-dependent entropy estimation of EEG rhythm changes following brain ischemia. Ann Biomed Eng 31:221–232

    Article  Google Scholar 

  110. Shin HC, Tong S, Yamashita S et al (Jun, 2006) Quantitative EEG and effect of hypothermia on brain recovery after cardiac arrest. IEEE Trans Biomed Eng 53(6):1016–23

    Article  Google Scholar 

  111. Weil MH, Becker L, Budinger T et al (2001) Workshop Executive Summary Report: Post-resuscitative and initial Utility in Life Saving Efforts (PULSE): June 29–30, 2000; Lansdowne Resort and Conference Center; Leesburg, VA. Circulation 103(9):1182–4

    Article  Google Scholar 

  112. Myerburg RJ, Castellanos A (2001) Hurst’s the heart, 10th edn. McGraw-Hill, New York

    Google Scholar 

  113. Morimoto Y, Kemmotsu O, Kitami K et al (1993) Acute brain swelling after out of hospital cardiac arrest: pathogenesis and outcome. Crit Care Med 21(1):104–110

    Article  Google Scholar 

  114. Safar P (1986) Cerebral resuscitation after cardiac arrest: a review. Circulation 74(6 Pt 2):IV138–53

    Google Scholar 

  115. Eisenberg MS, Mengert TJ (2001) Cardiac resuscitation. N Engl J Med 344(17):1304–13

    Article  Google Scholar 

  116. Vaagenes P, Ginsberg M, Ebmeyer U et al (Feb, 1996) Cerebral resuscitation from cardiac arrest: pathophysiologic mechanisms. Crit Care Med 24(2 Suppl):S57–68

    Google Scholar 

  117. Bedell S, Delbanco T, Cook E et al (1983) Survival after cardiopulmonary resuscitation in the hospital. N Engl J Med 309:569–576

    Article  Google Scholar 

  118. Berek K, Jeschow M, Aichner F (1997) The prognostication of cerebral hypoxia after out of hospital cardiac arrest in adults. Eur Neurol 37:135–145

    Article  Google Scholar 

  119. Ekstrom L, Herlitz J, Wennerblom B et al (1994) Survival after cardiac arrest outside hospital over a 12-year period in Gothenburg. Resuscitation 27(3):181–7

    Article  Google Scholar 

  120. Stiell IG, Wells GA, Field BJ et al (1999) Improved out-of-hospital cardiac arrest survival through the inexpensive optimization of an existing defibrillation program: OPALS study phase II. Ontario Prehospital Advanced Life Support. JAMA 281(13):1175–1181

    Article  Google Scholar 

  121. Grubb NR (2001) Managing out-of-hospital cardiac arrest survivors: 1. Neurological perspective. Heart 85(1):6–8

    Article  Google Scholar 

  122. Abramson NS, Safar P, Detre KM et al (Nov, 1985) Neurologic recovery after cardiac arrest: effect of duration of ischemia. Brain Resuscitation Clinical Trial I Study Group. Crit Care Med 13(11):930–1

    Article  Google Scholar 

  123. Mills SA (1995) Risk factors for cerebral injury and cardiac surgery. Ann Thoracic Surg 59:1296–1299

    Article  Google Scholar 

  124. White BC, Grossman LI, O’Neil BJ et al (1996) Global brain ischemia and reperfusion. Ann Emerg Med 27:588–594

    Article  Google Scholar 

  125. Longstreth W, Inui T, Cobb L et al (1983) Neurologic recovery after out of hospital cardiac arrest. Ann Int Med 98:588–592

    Google Scholar 

  126. Levy D, Bate D, Carrona J et al (1981) Prognosis in nontraumatic coma. Ann Int Med 94:293–301

    Google Scholar 

  127. AHA (2000) Guidelines 2000 for cardiopulmonary resuscitation and emergancy cardiovascular care

    Google Scholar 

  128. Becker LB, Weisfeldt ML, Weil MH et al (2002) The PULSE initiative: scientific priorities and strategic planning for resuscitation research and life saving therapies. Circulation 105(21):2562–70

    Article  Google Scholar 

  129. Brain Resuscitation Clinical Trial II Study Group (1991) A randomized clinical study of a calcium-entry blocker (lidoflazine) in the treatment of comatose survivors of cardiac arrest. Brain Resuscitation Clinical Trial II Study Group [see comments]. N Engl J Med 324(18):1225–1231

    Google Scholar 

  130. Bernard SA, Gray TW, Buist MD et al (2002) Treatment of comatose survivors of out-of-hospital cardiac arrest with induced hypothermia. N Engl J Med 346(8):557–63

    Article  Google Scholar 

  131. Bernard SA, Jones BM, Horne MK (1997) Clinical trial of induced hypothermia in comatose survivors of out-of-hospital cardiac arrest. Ann Emerg Med 30(2):146–53

    Article  Google Scholar 

  132. Longstreth WT Jr, Fahrenbruch CE, Olsufka M et al (2002) Randomized clinical trial of magnesium, diazepam, or both after out-of-hospital cardiac arrest. Neurology 59(4):506–14

    Article  Google Scholar 

  133. Shankaran S, Laptook AR, Ehrenkranz RA et al (2005) Whole-body hypothermia for neonates with hypoxic-ischemic encephalopathy. N Engl J Med 353(15):1574–84

    Article  Google Scholar 

  134. Zeiner A, Holzer M, Sterz F et al (2000) Mild resuscitative hypothermia to improve neurological outcome after cardiac arrest. A clinical feasibility trial. Hypothermia After Cardiac Arrest (HACA) Study Group. Stroke 31(1):86–94

    Article  Google Scholar 

  135. Nolan JP, Morley PT, Hoek TL et al (2003) Therapeutic hypothermia after cardiac arrest. An advisory statement by the Advancement Life support Task Force of the International Liaison committee on Resuscitation. Resuscitation 57(3):231–5

    Article  Google Scholar 

  136. Geocadin RG, Ghodadra R, Kimura T, Lei H, Sherman DL, Hanley DF, Thakor NV (2000) A novel quantitative EEG injury measure of global cerebral ischemia. Clin Neurophysiol 111(10):1779–1787

    Article  Google Scholar 

  137. Atit M, Sherman D (1999) Neonatal cortical injury monitor: phase I final report, USA

    Google Scholar 

  138. Sherman D, Brambrink A, Ichord R et al (1999) Quantitative EEG during early recovery from hypoxic-ischemia injury in immature piglets: burst occurrence and duration. Clin Electroenceph 30(4):175–183

    Google Scholar 

  139. Sherman DL, Atit MK, Geocadin RG et al (2002) Diagnostic instrumentation for neural injury. IEEE Instrum Meas 5:28–35

    Article  Google Scholar 

  140. Sherman DL, Hinich MJ, Thakor NV (1998) The higher order statistics of energy operators with applications to neurological signals. Proceedings of the IEEE-SP international symposium, Pittsburgh, PA, USA. Time-Frequency and Time-Scale Analysis, on 1998, 561–564

    Google Scholar 

  141. Kang X, Jia X, Geocadin RG et al (2009) Multiscale entropy analysis of EEG for assessment of post-cardiac arrest neurological recovery under hypothermia in rats. IEEE Trans Biomed Eng 56(4):1023–31

    Article  Google Scholar 

  142. Al-Nashash HA, Paul JS, Ziai WC et al (2003) Wavelet entropy for subband segmentation of EEG during injury and recovery. Ann Biomed Eng 31(6):653–8

    Article  Google Scholar 

  143. Al-Nashash HA, Thakor NV (2005) Monitoring of global cerebral ischemia using wavelet entropy rate of change. IEEE Trans Biomed Eng 52(12):2119–22

    Article  Google Scholar 

  144. Shin HC, Jia X, Nickl R et al (2008) A subband-based information measure of EEG during brain injury and recovery after cardiac arrest. IEEE Trans Biomed Eng 55(8):1985–90

    Article  Google Scholar 

  145. Jia X, Koenig MA, Shin HC et al (2006) Quantitative EEG and neurological recovery with therapeutic hypothermia after asphyxial cardiac arrest in rats. Brain Res 1111(1):166–75

    Article  Google Scholar 

  146. Cummins RO, Chamberlain D, Hazinski MF et al (1997) Recommended guidelines for reviewing, reporting, and conducting research on in-hospital resuscitation: the in-hospital “Utstein style”. American Heart Association. Ann Emerg Med 29(5):650–79

    Article  Google Scholar 

  147. Cummins RO, Chamberlain DA, Abramson NS et al (1991) Recommended guidelines for uniform reporting of data from out-of-hospital cardiac arrest: the Utstein Style. Task Force of the American Heart Association, the European Resuscitation Council, the Heart and Stroke Foundation of Canada, and the Australian Resuscitation Council. Ann Emerg Med 20(8):861–74

    Article  Google Scholar 

  148. (1986) Randomized clinical study of thiopental loading in comatose survivors of cardiac arrest. Brain Resuscitation Clinical Trial I Study Group. N Engl J Med 314(7): 397–403

    Google Scholar 

  149. (1986) A randomized clinical study of cardiopulmonary–cerebral resuscitation: design, methods, and patient characteristics. Brain Resuscitation Clinical Trial I Study Group. Am J Emerg Med 4(1):72–86

    Google Scholar 

  150. Abramson NS, Safar P, Detre K et al (1982) An international collaborative clinical study mechanism for resuscitation research. Resuscitation 10(2):141–7

    Article  Google Scholar 

  151. Jastremski M, Sutton-Tyrrell K, Vaagenes P et al (1989) Glucocorticoid treatment does not improve neurological recovery following cardiac arrest. Brain Resuscitation Clinical Trial I Study Group. Jama 262(24):3427–30

    Article  Google Scholar 

  152. Hypothermia after Cardiac Arrest Study Group (2002) Mild therapeutic hypothermia to improve the neurologic outcome after cardiac arrest. N Engl J Med 346(8):549–56

    Article  Google Scholar 

  153. Zandbergen EG, de Haan RJ, Koelman JH et al (2000) Prediction of poor outcome in anoxic-ischemic coma. J Clin Neurophysiol 17(5):498–501

    Article  Google Scholar 

  154. Zandbergen EG, de Haan RJ, Stoutenbeek CP et al (1998) Systematic review of early prediction of poor outcome in anoxic-ischaemic coma. Lancet 352(9143):1808–12

    Article  Google Scholar 

  155. Edgren E, Hedstrand U, Kelsey S et al (1994) Assessment of neurological prognosis in comatose survivors of cardiac arrest. BRCT I Study Group. Lancet 343(8905):1055–9

    Article  Google Scholar 

  156. FDA (2011) Seizure detection, cognitive function and TBI/concussion devices: issues in their evaluation. In: Workshop, FDA White Oak Campus, Silver Spring, MD

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Biomedical Engineering Department, Johns Hopkins University, SINAPSE Institute, National University of Singapore, Baltimore, MD, USA and Singapore

    Nitish V. Thakor

  2. Johns Hopkins University, Baltimore, MD, USA

    David L. Sherman

Authors
  1. Nitish V. Thakor
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David L. Sherman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nitish V. Thakor .

Editor information

Editors and Affiliations

  1. Deptarment of Biomedical Engineering, 7-105 Hasselmo Hall, University of Minnesota, 312 Church Street SE., Minneapolis, 55455, Minnesota, USA

    Bin He

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media New York

About this chapter

Cite this chapter

Thakor, N.V., Sherman, D.L. (2013). EEG Signal Processing: Theory and Applications. In: He, B. (eds) Neural Engineering. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-5227-0_5

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-5227-0_5

  • Published:

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4614-5226-3

  • Online ISBN: 978-1-4614-5227-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation